@inproceedings{34625,
  keywords = {Thermal Technologies},
  author = {Morgan A Mayer and Tyler Huntington and Ana Comesana and Vi H Rapp and Kyle Niemeyer},
  title = {Can machine learning predict fuel properties accurately?},
  abstract = {<p>High-potential molecules derived from biomass sources may suitably replace or supple-ment traditional nonrenewable hydrocarbon fuels to reduce pollution and fuel processing cost. Ex-perimental property testing of these bioproducts is usually conducted years after initial bench-scale experiments, due to high experimental costs and/or high volume requirements. However, neglecting to conduct property testing early in the pathway development cycle can lead to investments spent on scaling-up production of bioproducts and biofuels that do not perform as expected. Instead, machine-learning techniques can be used to develop quantitative structure–property relationships for molecules using a relatively large training set of molecular descriptor data. For this study, we compiled measured properties, IR spectra, and molecular descriptors of bio-based molecules from databases and published studies for training models of bioproduct properties. We trained regres-sion models with molecular descriptors and will compare results of different estimators. This study describes the first steps towards a performance prediction tool for bio-based alternative fuels. Keywords: Machine learning, biofuels, jet fuels, fuel properties</p>},
  year = {2019},
  journal = {2019 WSSCI Fall Technical Meeting},
  month = {10/2019},
  language = {eng},
}